Bossard Céline, Salhi Yahia, Khammari Amir, Brousseau Maud, Le Corre Yannick, Salhi Sanae, Quéreux Gaëlle, Chetritt Jérôme J
Pathology Department, IHP Group, Nantes, France.
DiaDeep, Lyon, France.
J Eur Acad Dermatol Venereol. 2025 Aug;39(8):1500-1509. doi: 10.1111/jdv.20538. Epub 2025 Jan 24.
There is a need to improve risk stratification of primary cutaneous melanomas to better guide adjuvant therapy. Taking into account that haematoxylin and eosin (HE)-stained tumour tissue contains a huge amount of clinically unexploited morphological informations, we developed a weakly-supervised deep-learning approach, SmartProg-MEL, to predict survival outcomes in stages I to III melanoma patients from HE-stained whole slide image (WSI).
We designed a deep neural network that extracts morphological features from WSI to predict 5-y overall survival (OS), and assign a survival risk score to each patient. The model was trained and validated on a discovery cohort of primary cutaneous melanomas (IHP-MEL-1, n = 342). Performance was tested on two external and independent datasets (IHP-MEL-2, n = 161; and TCGA cohort n = 63). It was compared with well-established prognostic factors. Concordance index (c-index) was used as a metric.
On the discovery cohort, the SmartProg-MEL predicts the 5-y OS with a c-index of 0.78 on the cross-validation data and of 0.72 on the cross-testing series. In the external cohorts, the model achieved a c-index of 0.71 and 0.69 for the IHP-MEL-2 and TCGA dataset respectively. Furthermore, SmartProg-MEL was an independent and the most powerful prognostic factor in multivariate analysis (HR = 1.84, p-value < 0.005). Finally, the model was able to dichotomize patients in two groups-a low and a high-risk group-each associated with a significantly different 5-y OS (p-value < 0.001 for IHP-MEL-1 and p-value = 0.01 for IHP-MEL-2).
The performance of our fully automated SmartProg-MEL model outperforms the current clinicopathological factors in terms of prediction of 5-y OS and risk stratification of cutaneous melanoma patients. Incorporation of SmartProg-MEL in the clinical workflow could guide the decision-making process by improving the identification of patients that may benefit from adjuvant therapy.
需要改进原发性皮肤黑色素瘤的风险分层，以更好地指导辅助治疗。考虑到苏木精和伊红（HE）染色的肿瘤组织包含大量尚未得到临床应用的形态学信息，我们开发了一种弱监督深度学习方法SmartProg-MEL，用于从HE染色的全切片图像（WSI）预测I至III期黑色素瘤患者的生存结果。
我们设计了一个深度神经网络，从WSI中提取形态学特征以预测5年总生存期（OS），并为每位患者分配一个生存风险评分。该模型在原发性皮肤黑色素瘤发现队列（IHP-MEL-1，n = 342）上进行训练和验证。在两个外部独立数据集（IHP-MEL-2，n = 161；和TCGA队列，n = 63）上测试其性能。将其与成熟的预后因素进行比较。一致性指数（c-index）用作衡量指标。
在发现队列中，SmartProg-MEL在交叉验证数据上预测5年OS的c-index为0.78，在交叉测试系列中为0.72。在外部队列中，该模型在IHP-MEL-2和TCGA数据集上的c-index分别为0.71和0.69。此外，在多变量分析中，SmartProg-MEL是一个独立且最强大的预后因素（HR = 1.84，p值<0.005）。最后，该模型能够将患者分为两组——低风险组和高风险组——每组的5年OS有显著差异（IHP-MEL-1的p值<0.001，IHP-MEL-2的p值 = 0.01）。
我们的全自动SmartProg-MEL模型在预测5年OS和皮肤黑色素瘤患者风险分层方面的性能优于当前的临床病理因素。将SmartProg-MEL纳入临床工作流程可以通过改善对可能从辅助治疗中受益的患者的识别来指导决策过程。
